1,136 件 ・ 関連度順
概要と使いどころ
Explicit invocation only; never for abusing third-party infrastructure. Generates ranked, owner-controlled VPN/proxy protocol and topology experiment ideas for restrictive networks (TURN/ICE/MASQUE/QUIC/WebTransport).
日本語の概要は準備中です。原文の説明を表示しています。
Act as an end-to-end data scientist: turn business questions into defensible analysis, validated models, and decision-ready reports. Use whenever the user asks to analyze, explore, or profile a dataset or CSV/Parquet/Excel file; asks what drives a metric or why a number changed ("why did churn go up?"); wants to test whether a difference is real (A/B tests, experiments, "is this significant?", "how many samples do I need?"); wants a predictive model (churn, forecast, scoring, segmentation, classification, regression); asks to review an existing analysis, notebook, or model for flaws; or needs results written up for decision-makers. Triggers in any language ("phân tích dữ liệu", "xây model dự đoán", "kiểm định A/B"), even when they never say "data science" or "statistics".
日本語の概要は準備中です。原文の説明を表示しています。
Design an A/B test — power analysis, randomization, and success metrics. Use when asked to "design an A/B test", "how many users do we need", or "run a power analysis".
日本語の概要は準備中です。原文の説明を表示しています。
Provides chaos engineering best practices for resilience testing, fault injection, and game day planning. Use when designing resilience experiments, configuring chaos tools, planning game days, or when user mentions 'chaos engineering', 'resilience', 'litmus', 'game day', 'fault injection', 'chaos monkey', 'blast radius', 'steady state', 'failure mode'.
日本語の概要は準備中です。原文の説明を表示しています。
Conjecture generation, sequence analysis, and experimental mathematics for mathematical discovery. Covers sequence analysis (OEIS lookups, recurrence relations, generating functions), pattern detection methods (finite differences, ratio analysis, modular patterns), conjecture generation workflow, classical integer sequences (Fibonacci, Catalan, partition numbers, Bernoulli numbers), combinatorial identities, the probabilistic method (Erdos, expected value arguments, Lovasz Local Lemma), AI-assisted discovery (FunSearch, Ramanujan Machine), and confidence calibration with cautionary tales (Mertens conjecture, Polya conjecture). Use when discovering patterns, analyzing sequences, generating conjectures, or working with combinatorial identities.
日本語の概要は準備中です。原文の説明を表示しています。
Color theory principles for art education. Covers the three color properties (hue, saturation, value), color mixing systems (subtractive and additive), color relationships (complementary, analogous, triadic, split-complementary), color temperature, simultaneous contrast and the relativity of color perception, and practical palette construction. Use when analyzing color in artworks, planning color schemes, understanding optical phenomena in painting, or investigating Albers's Interaction of Color experiments.
日本語の概要は準備中です。原文の説明を表示しています。
Quantum computing framework for building, simulating, optimizing, and executing quantum circuits. Use this skill when working with quantum algorithms, quantum circuit design, quantum simulation (noiseless or noisy), running on quantum hardware (Google, IonQ, AQT, Pasqal), circuit optimization and compilation, noise modeling and characterization, or quantum experiments and benchmarking (VQE, QAOA, QPE, randomized benchmarking).
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.
日本語の概要は準備中です。原文の説明を表示しています。
Use when facing unknowns, debugging without a clear cause, or making architecture decisions — enforces hypothesis-driven scientific reasoning through observation, hypothesis formulation, prediction, experiment design, and evidence-based conclusion. Use when previous attempts have failed or the problem space involves uncertainty.
日本語の概要は準備中です。原文の説明を表示しています。
Trace bugs, CI failures, and unexpected behavior from reproduced evidence to a causal mechanism and governing contract. Distinguish product, architecture, test-oracle, and harness defects; investigate intermittent failures with controlled experiments and report unresolved evidence explicitly.
日本語の概要は準備中です。原文の説明を表示しています。
Look up prior Claude Code sessions when context is lost or forgotten. Use when asked what was done before, what happened in the last session, or any request to recall past conversation history, prior decisions, experiments, or outcomes. Indexes and searches raw JSONL transcripts from ~/.claude/projects/ via DuckDB. Returns verbatim user messages, summarizes AI actions and sub-agent outcomes, detects tool errors and user frustration signals, and reports tool usage statistics. Summaries cached at ~/.claude/kaizen/session-summaries/.
日本語の概要は準備中です。原文の説明を表示しています。
Opt-in recreation of the Bun + TypeScript framework used to build Vapi's landing-page voice agents from a ROUGH_DRAFT.md spec, including a scenario registry, language stacks, prompt composer, assistant builder, and idempotent bootstrap script. Use only when the user explicitly invokes vapi-bootstrap-framework or specifically asks to reproduce or experiment with the landing-page-agent architecture. Do not use for ordinary Vapi assistant builds; use create-assistant, create-tool, create-squad, and vapi-prompt-builder instead. Targets Bun + TypeScript + @vapi-ai/server-sdk.
日本語の概要は準備中です。原文の説明を表示しています。
Use for Deepline GTM work that searches, enriches, scores, collects signals, or automates a workflow: find companies or people, enrich a CSV, find emails or LinkedIn, compare providers, build a waterfall, create a webhook or cron, or write a Play. For live information work, run a small heterogeneous experiment, exploit the observed winner, and reopen misses. Skip pure copywriting and non-GTM research.
日本語の概要は準備中です。原文の説明を表示しています。
Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.
日本語の概要は準備中です。原文の説明を表示しています。
Designs survey instruments, covering question wording, scale construction and index reliability, flow and treatment placement, pretesting and soft launches, respondent burden with attention and speeding rules, sensitive questions and social-desirability bias, and treatment delivery. Use when the user writes or revises survey questions, chooses a scale, orders blocks, plans a pilot, or worries respondents will not answer honestly. Indirect measurement goes to list-experiment, multi-country instruments to cross-national-design, and live-survey operations to qualtrics-ops.
日本語の概要は準備中です。原文の説明を表示しています。
Advises on setting up and validating topic models for survey and experimental text. Covers the discovery-versus-measurement question, choosing STM, LDA, or BERTopic, preprocessing consequences, prevalence and content formulas, topic-count selection across coherence, exclusivity and FREX, held-out likelihood, and residuals, validation of topics against human judgment, robustness, and DA-RT-compliant reporting. Use when the user has open-ended responses or another corpus and asks what topics are present, how many topics to use, about STM, searchK, coherence, or FREX, or wants topic prevalence compared across treatment arms or countries. Fixed-codebook coding goes to text-classification.
日本語の概要は準備中です。原文の説明を表示しています。
Audits a methods section against a 45-item reporting checklist synthesized from the APSA Experimental Section rubric, JARS-Quant, CONSORT, and DA-RT, covering pre-registration, recruitment, randomization and treatments, sample flow and attrition, sample-size justification, three-tier results labeling, conjoint reporting, validity, and open-science infrastructure. Use when checking whether a methods section reports enough, preparing a submission or replication archive, asking what CONSORT, JARS, or DA-RT require, or documenting a deviation from a pre-analysis plan. Writing that plan before data collection goes to pre-registration-writing.
日本語の概要は準備中です。原文の説明を表示しています。
Audit a methods section against a 45-item checklist drawn from the APSA Experimental Section rubric, JARS-Quant, CONSORT, and DA-RT. Use before submission or archiving, or to document a pre-analysis-plan deviation.
日本語の概要は準備中です。原文の説明を表示しています。
Drafts or audits a scientific paper's introduction: real-world motivation, evidence audit, theory-to-if-then funnel with a named estimand, bridges across experiments, and calibrated contributions. Use to write, frame, or critique an introduction.
日本語の概要は準備中です。原文の説明を表示しています。
Advises on setting up and validating topic models for survey and experimental text: discovery vs. measurement, STM/LDA/BERTopic choice, preprocessing, covariate formulas, choosing K across coherence, FREX, held-out likelihood, and residuals, validating topics against human coding, robustness, and reporting. Coding against a fixed codebook goes to text-classification.
日本語の概要は準備中です。原文の説明を表示しています。
Designs conjoint and factorial-vignette experiments, covering attribute architecture and randomization restrictions, effective-N power, treatment realism, the choice among AMCE, marginal-mean, and AMIE estimands, forced-choice versus rating designs, and PAP tiers. Use when the user is planning a conjoint, drafting or critiquing an attribute table, asking how many respondents or tasks are needed, whether to report AMCEs or marginal means, or how to test interactions. Reviewing an existing design goes to conjoint-diagnostics, cleaning the export to conjoint-cleaning.
日本語の概要は準備中です。原文の説明を表示しています。
Designs conjoint and factorial-vignette experiments: attributes and restrictions, effective-N power, estimand choice among AMCE, marginal means, and AMIE, forced choice versus rating, and PAP tiers. Use when planning a conjoint, critiquing an attribute table, sizing respondents and tasks, or testing interactions.
日本語の概要は準備中です。原文の説明を表示しています。
Run the Anabasis v2 improvement loop, the Super Loop: find the one link that holds the climb, choose one change against it, prove its path, put a prediction on record, launch, watch, read the log, decide the next move. Use when the operator asks to keep a campaign going, to run a pass of the loop, what to launch next, why a campaign is not climbing, or what a finished round means for the next one. Owns the choice of experiment and the reading of its evidence; the operator owns promotion, a freeze, a stop, spend and the solve's condition.
日本語の概要は準備中です。原文の説明を表示しています。
Expert chaos engineer specializing in controlled failure injection, resilience testing, and building antifragile systems. Masters chaos experiments, game day planning, and continuous resilience improvement with focus on learning from failure.
日本語の概要は準備中です。原文の説明を表示しています。